The cuproptosis-related signature predicts the prognosis and immune microenvironments of primary diffuse gliomas: a comprehensive analysis.

BACKGROUND: Evidence has revealed a connection between cuproptosis and the inhibition of tumor angiogenesis. While the efficacy of a model based on cuproptosis-related genes (CRGs) in predicting the prognosis of peripheral organ tumors has been demonstrated, the impact of CRGs on the prognosis and the immunological landscape of gliomas remains unexplored. METHODS: We screened CRGs to construct a novel scoring tool and developed a prognostic model for gliomas within the various cohorts. Afterward, a comprehensive exploration of the relationship between the CRG risk signature and the immunological landscape of gliomas was undertaken from multiple perspectives. RESULTS: Five genes (NLRP3, ATP7B, SLC31A1, FDX1, and GCSH) were identified to build a CRG scoring system. The nomogram, based on CRG risk and other signatures, demonstrated a superior predictive performance (AUC of 0.89, 0.92, and 0.93 at 1, 2, and 3 years, respectively) in the training cohort. Furthermore, the CRG score was closely associated with various aspects of the immune landscape in gliomas, including immune cell infiltration, tumor mutations, tumor immune dysfunction and exclusion, immune checkpoints, cytotoxic T lymphocyte and immune exhaustion-related markers, as well as cancer signaling pathway biomarkers and cytokines. CONCLUSION: The CRG risk signature may serve as a robust biomarker for predicting the prognosis and the potential viability of immunotherapy responses. Moreover, the key candidate CRGs might be promising targets to explore the underlying biological background and novel therapeutic interventions in gliomas.

Exploring and clinical validation of prognostic significance and therapeutic implications of copper homeostasis-related gene dysregulation in acute myeloid leukemia.

The patterns and biological functions of copper homeostasis-related genes (CHRGs) in acute myeloid leukemia (AML) remain unclear. We explored the patterns and biological functions of CHRGs in AML. Using independent cohorts, including TCGA-GTEx, GSE114868, GSE37642, and clinical samples, we identified 826 common differentially expressed genes. Specifically, 12 cuproptosis-related genes (e.g., ATP7A, ATP7B) were upregulated, while 17 cuproplasia-associated genes (e.g., ATOX1, ATP7A) were downregulated in AML. We used LASSO-Cox, Kaplan-Meier, and Nomogram analyses to establish prognostic risk models, effectively stratifying patients with AML into high- and low-risk groups. Subgroup analysis revealed that high-risk patients exhibited poorer overall survival and involvement in fatty acid metabolism, apoptosis, and glycolysis. Immune infiltration analysis indicated differences in immune cell composition, with notable increases in B cells, cytotoxic T cells, and memory T cells in the low-risk group, and increased monocytes and neutrophils in the high-risk group. Single-cell sequencing analysis corroborated the expression characteristics of critical CHRGs, such as MAPK1 and ATOX1, associated with the function of T, B, and NK cells. Drug sensitivity analysis suggested potential therapeutic agents targeting copper homeostasis, including Bicalutamide and Sorafenib. PCR validation confirmed the differential expression of 4 cuproptosis-related genes (LIPT1, SLC31A1, GCSH, and PDHA1) and 9 cuproplasia-associated genes (ATOX1, CCS, CP, MAPK1, SOD1, COA6, PDK1, DBH, and PDE3B) in AML cell line. Importantly, these genes serve as potential biomarkers for patient stratification and treatment. In conclusion, we shed light on the expression patterns and biological functions of CHRGs in AML. The developed risk models provided prognostic implications for patient survival, offering valuable information on the regulatory characteristics of CHRGs and potential avenues for personalized treatment in AML.

Identification of a novel cuproptosis-related gene signature for rheumatoid arthritis-A prospective study.

BACKGROUND: Rheumatoid arthritis (RA) is a multifactorial systemic autoimmune disease characterized by ongoing synovial inflammation, leading to the degradation of cartilage. Cuproptosis, as a newly characterized form of cell death, may influence RA progression by regulating immune cells and chondrocytes. This study sets out to identify the hub cuproptosis-related gene (CRG) associated with the pathogenesis of RA. METHODS: A series of bioinformatic analyses were performed to evaluate the expression score of CRGs and the immune infiltration landscape between RA and normal samples. The hub gene was screened through the correlation analysis of CRGs, and the interaction network between the hub gene and transcription factors (TFs) was constructed. Finally, the hub gene was validated through quantitative real-time polymerase chain reaction (qRT-PCR) of patient samples and cell experiments. RESULTS: Drolipoamide S-acetyltransferase (DLAT) was screened as the hub gene. Correlation analysis between the hub gene and immune microenvironment demonstrated that DLAT had the highest correlation with T follicular helper cells. Eight pairs of DLAT-TF interaction networks were constructed. Single-cell sequencing showed that CRGs were highly expressed in RA chondrocytes, and chondrocytes could be classified into three different subsets. qRT-PCR was used to validate the above results. Dlat knockdown in immortalized human chondrocytes led to significantly improved mitochondrial membrane potentials and reduced levels of intracellular reactive oxygen species (ROS), mitochondrial ROS and apoptosis. CONCLUSIONS: This study rudimentarily demonstrates the correlation between CRGs and immune cell infiltration in RA. The biomarker DLAT may provide comprehensive insights into the pathogenesis and drug targets of RA.

Leveraging a cuproptosis-based signature to predict the prognosis and drug sensitivity of cutaneous melanoma.

Immunotherapy is a vital treatment for patients with cutaneous melanoma (CM), but effective predictors to guide clinical immunotherapy are lacking. Cuproptosis is a newly discovered mode of cell death related to tumorigenesis. Exploring the relationship between the mode of cuproptosis and the effect of immunotherapy on CM could better guide clinical management. We clustered all patients with CM in the Cancer Genome Atlas (TCGA) database based on cuproptosis-related genes (CRGs). Prognosis, immunotherapeutic effect, tumor microenvironment score, expression of CD274, CTLA4, and PDCD1, and abundance of CD8 + T infiltration in group A were higher than in group B. Using a combination of LASSO and COX regression analysis, we identified 10 molecules significant to prognosis from differentially expressed genes between the two groups and constructed a cuproptosis-related scoring system (CRSS). Compared with the American Joint Committee on Cancer (AJCC) staging system, CRSS more accurately stratified CM patient risk and guided immunotherapy. CRSS successfully stratified risk and predicted the effect of immunotherapy in 869 patients with eight CM immunotherapy datasets and multiple other tumor immunotherapy cohorts. The nomogram model, which combined AJCC stage and CRSS, greatly improved the ability and accuracy of prognosis prediction. In general, our cuproptosis-related scoring system and nomogram model accurately stratified risk in CM patients and effectively predicted prognosis and the effect of immunotherapy in CM patients.

Exploring the role of LIAS-related cuproptosis in systemic lupus erythematosus.

BACKGROUND: Cuproptosis is a novel mode of cell death, which is strongly related to energy metabolism in mitochondria and regulated by protein lipoylation. Currently, the molecular mechanisms of cuproptosis-related genes (CRGs) involved in systemic lupus erythematosus (SLE) largely remained unclear, our study is aimed to explore the mechanisms of cuproptosis and CRGs involved in SLE. METHODS: Bulk RNA-seq datasets were collected to display the expressions of CRGs in peripheral blood mononuclear cells (PBMCs) of SLE and healthy individuals, and then ROC analysis was used to establish the diagnostic models of CRGs. Next, the immune infiltration analyses were applied to reveal the difference of immune cells infiltration in LIAS-low and LIAS-high group. Additionally, WGCNA analysis was performed to find the gene modules significantly correlated with the LIAS expression level. We also performed the functional enrichment analyses for LIAS-related gene modules to determine the potential pathways involved in the development of SLE. Finally, scRNA-seq dataset was used to cluster immune cell subsets, reveal the activated pathways, and study cell-cell interactions in LIAS-low and LIAS-high cells. RESULT: We found CDKN2A was significantly increased and LIAS was significantly decreased in SLE patients compared with healthy individuals. The AUC score showed that LIAS had a great diagnostic value than other CRGs. Additionally, the results of immune infiltration analyses showed that immune cells proportion were diverse in LIAS-low and LIAS-high samples. The gene sets related to LIAS expression level were involved in dephosphorylation of JAK1 by SHP1, phosphorylation of STAT2, cytokine signaling in immune system, expression of interferon-alpha and beta, inhibition of JAK kinase activity by SOCS1/3, and so on. Finally, the results of cell-cell communication showed that CCL- (CCL5 + CCR1) and ANNEXIN- (ANXA1 + FPR1) might play an essential role in the communication network between LIAS-low and LIAS-high cells. CONCLUSION: Above findings inferred that LIAS-mediated cuproptosis might involve in a comprehensive cellular and molecular mechanism to cause the occurrence and development of SLE.

Construction of a novel cuproptosis-related gene signature for predicting prognosis and estimating tumor immune microenvironment status in papillary thyroid carcinoma.

BACKGROUND: Cuproptosis, a new form of programmed cell death, has been recently reported to be closely related to tumor progression. However, the significance of cuproptosis-related genes (CRGs) in papillary thyroid carcinoma (PTC) is still unclear. Therefore, this study aimed to investigate the role of the CRG signature in prognosis prediction and immunotherapeutic effect estimation in patients with PTC. METHODS: RNA-seq data and the corresponding clinical information of patients with PTC were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Comprehensive analyses, namely, consensus clustering, immune analyses, functional enrichment, least absolute shrinkage and selection operator-multivariate Cox regression, and nomogram analysis, were performed to identify new molecular subgroups, determine the tumor immune microenvironment (TIME) status of the identified subgroups, and construct a clinical model. Independent verification cohort data and quantitative real-time polymerase chain reaction (qPCR) was performed to validate the expression of specific prognosis-related and differentially expressed CRGs (P-DECRGs). RESULTS: In the TCGA database, 476 patients with PTC who had complete clinical and follow-up information were included. Among 135 CRGs, 21 were identified as P-DECRGs. Two molecular subgroups with significantly different disease-free survival and TIME statuses were identified based on these 21 P-DECRGs. The differentially expressed genes between the two subgroups were mainly associated with immune regulation. The risk model and nomogram were constructed based on four specific P-DECRGs and validated as accurate prognostic predictions and TIME status estimation for PTC by TCGA and GEO verification cohorts. Finally, the qPCR results of 20 PTC and paracancerous thyroid tissues validated those in the TCGA database. CONCLUSIONS: Four specific P-DECRGs in PTC were identified, and a clinical model based on them was established, which may be helpful for individualized immunotherapeutic strategies and prognostic prediction in patients with PTC.

Exploring the cuproptosis-related molecular clusters in the peripheral blood of patients with amyotrophic lateral sclerosis.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a progressive and lethal neurodegenerative disease. Several studies have suggested the involvement of cuproptosis in its pathogenesis. In this research, we intend to explore the cuproptosis-related molecular clusters in ALS and develop a novel cuproptosis-related genes prediction model. METHODS: The peripheral blood gene expression data was downloaded from the Gene Expression Omnibus (GEO) online database. Based on the GSE112681 dataset, we investigated the critical cuproptosis-related genes (CuRGs) and pathological clustering of ALS. The immune microenvironment features of the peripheral blood in ALS patients were also examined using the CIBERSORT algorithm. Cluster-specific hub genes were determined by the WGCNA. The most accurate prediction model was selected by comparing the performance of four machine learning techniques. ROC curves and two independent datasets were applied to validate the prediction accuracy. The available compounds targeting these hub genes were filtered to investigate their efficacy in treating ALS. RESULTS: We successfully determined four critical cuproptosis-related genes and two pathological clusters with various immune profiles and biological characteristics in ALS. Functional analysis showed that genes in Cluster1 were primarily enriched in pathways closely associated with immunity. The Support Vector Machine (SVM) model exhibited the best discrimination properties with a large area under the curve (AUC = 0.862). Five hub prediction genes (BAP1, SMG1, BCLAF1, DHX15, EIF4G2) were selected to establish a nomogram model, suggesting significant risk prediction potential for ALS. The accuracy of this model in predicting ALS incidence was also demonstrated through calibration curves, nomograms, and decision curve analysis. Finally, three drugs targeting BAP1 were determined through drug-gene interactions. CONCLUSION: This study elucidated the complex associations between cuproptosis and ALS and constructed a satisfactory predictive model to explore the pathological characteristics of different clusters in ALS patients.

Identification of diagnostic signature and immune infiltration for ischemic cardiomyopathy based on cuproptosis-related genes through bioinformatics analysis and experimental validation.

BACKGROUND: Ischemic cardiomyopathy (IC) is primarily due to long-term ischemia/hypoxia of the coronary arteries, leading to impaired cardiac contractile or diastolic function. A new form of cell death induced by copper, called "cuproptosis" is related to the development and progression of multiple diseases. The cuproptosis-related gene (CuGs) plays an important role in acute myocardial infarction, while the specific mechanisms of CuGs in ischemic cardiomyopathy remain unclear. METHODS: The expressions of CuGs and their immune characteristics were analyzed with the IC datasets obtained from the Gene Expression Omnibus, namely GSE5406 and GSE57338, identifying core genes associated with IC development. By comparing RF, SVM, GLM and XGB models, the optimal machine learning model was selected. The expression of marker genes was validated based on the GSE57345, GSE48166 and GSE42955 datasets. Construct a CeRNA network based on core genes. Therapeutic chemiacals targeting core genes were acquired using the CTD database, and molecular docking was performed using Autodock vina software. By ligating the left anterior descending (LAD) coronary artery, an IC mouse model is established, and core genes were experimentally validated using Western blot (WB) and immunohistochemistry (IHC) methods. RESULTS: We identified 14 CuGs closely associated with the onset of IC. The SVM model exhibited superior discriminative power (AUC = 0.914), with core genes being DLST, ATP7B, FDX1, SLC31A1 and DLAT. Core genes were validated on the GSE42955, GSE48166 and GSE57345 datasets, showing excellent performance (AUC = 0.943, AUC = 0.800, and AUC = 0.932). The CeRNA network consists of 218 nodes and 264 lines, including 5 core diagnostic genes, 52 miRNAs, and 161 lncRNAs. Chemicals predictions indicated 8 chemicals have therapeutic effects on the core diagnostic genes, with benzo(a)pyrene molecular docking showing the highest affinity (-11.3 kcal/mol). Compared to the normal group, the IC group,which was established by LAD ligation, showed a significant decrease in LVEF as indicated by cardiac ultrasound, and increased fibrosis as shown by MASSON staining, WB results suggest increased expression of DLST and ATP7B, and decreased expression of FDX1, SLC31A1 and DLAT in the myocardial ischemic area (p < 0.05), which was also confirmed by IHC in tissue sections. CONCLUSION: In summary, this study comprehensively revealed that DLST, ATP7B, FDX1, SLC31A1 and DLAT could be identified as potential immunological biomarkers in IC, and validated through an IC mouse model, providing valuable insights for future research into the mechanisms of CuGs and its diagnostic value to IC.

FDX1 as a novel biomarker and treatment target for stomach adenocarcinoma.

BACKGROUND: Stomach adenocarcinoma (STAD) is one of the main reasons for cancer-related deaths worldwide. This investigation aimed to define the connection between STAD and Cuproptosis-related genes (CRGs). Cuproptosis is a newly identified form of mitochondrial cell death triggered by copper. AIM: To explore the identification of potential biomarkers for STAD disease based on cuproptosis. METHODS: A predictive model using Gene Ontology (GO), Least Absolute Shrinkage and Selection Operator (LASSO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Variation Analysis (GSVA), and Gene Set Enrichment Analysis analyzed gene interconnections, focusing on 3 copper-related genes and their expression in The Cancer Genome Atlas-STAD. Networks for mRNA-miRNA and mRNA-transcription factor interactions were constructed. The prognostic significance of CRG scores was evaluated using time-receiver operating characteristic, Kaplan-Meier curves, and COX regression analysis. Validation was conducted with datasets GSE26942, GSE54129, and GSE66229. Expression of copper-related differentially expressed genes was also analyzed in various human tissues and gastric cancer subpopulations using the human protein atlas. RESULTS: Three significant genes (FDX1, LIAS, MTF1) were identified and selected via LASSO analysis to predict and classify individuals with STAD into high and low CRG score subgroups. These genes were down-regulated in both risk categories. GO and KEGG analyses highlighted their involvement mainly in the electron transport chain. After validating their differential expression, FDX1 emerged as the most accurate diagnostic marker for gastric cancer. Additionally, the RCircos package localized FDX1 on chromosome 11. CONCLUSION: Our study revealed that FDX1 could be a potential biomarker and treatment target for gastric malignancy, providing new ideas for further scientific research.

Deciphering the molecular classification of pediatric sepsis: integrating WGCNA and machine learning-based classification with immune signatures for the development of an advanced diagnostic model.

Introduction: Pediatric sepsis (PS) is a life-threatening infection associated with high mortality rates, necessitating a deeper understanding of its underlying pathological mechanisms. Recently discovered programmed cell death induced by copper has been implicated in various medical conditions, but its potential involvement in PS remains largely unexplored. Methods: We first analyzed the expression patterns of cuproptosis-related genes (CRGs) and assessed the immune landscape of PS using the GSE66099 dataset. Subsequently, PS samples were isolated from the same dataset, and consensus clustering was performed based on differentially expressed CRGs. We applied weighted gene co-expression network analysis to identify hub genes associated with PS and cuproptosis. Results: We observed aberrant expression of 27 CRGs and a specific immune landscape in PS samples. Our findings revealed that patients in the GSE66099 dataset could be categorized into two cuproptosis clusters, each characterized by unique immune landscapes and varying functional classifications or enriched pathways. Among the machine learning approaches, Extreme Gradient Boosting demonstrated optimal performance as a diagnostic model for PS. Discussion: Our study provides valuable insights into the molecular mechanisms underlying PS, highlighting the involvement of cuproptosis-related genes and immune cell infiltration.

Comprehensiveness cuproptosis related genes study for prognosis and medication sensitiveness across cancers, and validation in prostate cancer.

Cuproptosis-related genes (CRGs) are important for tumor development. However, the functions of CRGs across cancers remain obscure. We performed a pan-cancer investigation to reveal the roles of CRGs across cancers. In an analysis of 26 cancers, 12 CRGs were differentially expressed, and those CRGs were found to have prognostic value across different cancer types. The expression of CRGs exhibited varied among tumors of 6 immune subtypes and were significantly correlated with the 16 sensitivities of drugs. The expression of CRGs were highly correlated with immunological subtype and tumor microenvironment (TME) of prostate cancer. We also established CRGs-related prognostic signatures that closely correlated with prognosis and drug sensitivity of prostate cancer patients. Single-cell RNA-seq revealed that several CRGs were enriched in the cancer cells. Finally, an in vitro experiment showed that elesclomol, a cuproptosis inducer, targets ferredoxin 1 and suppress cell viability in prostate cancer cells. In conclusion, we carried out a comprehensive investigation for determining CRGs in differential expression, prognosis, immunological subtype, TME, and cancer treatment sensitivity across 26 malignancies; and validated the results in prostate cancer. Our research improves pan-cancer knowledge of CRGs and identifies more effective immunotherapy strategies.

Single-cell and bulk RNA-seq unveils the immune infiltration landscape associated with cuproptosis in cerebral cavernous malformations.

BACKGROUND: Cerebral cavernous malformations (CCMs) are vascular abnormalities associated with deregulated angiogenesis. Their pathogenesis and optimal treatment remain unclear. This study aims to investigate the molecular signatures of cuproptosis, a newly identified type of cell death, associated with CCMs development. METHODS: Bulk RNA sequencing (RNA-seq) from 15 CCM and 6 control samples were performed with consensus clustering and clustered to two subtypes based on expression levels of cuproptosis-related genes (CRGs). Differentially expressed genes and immune infiltration between subtypes were then identified. Machine learning algorithms including the least absolute shrinkage and selection operator and random forest were employed to screen for hub genes for CCMs associated with cuproptosis. Furthermore, Pathway enrichment and correlation analysis were used to explore the functions of hub genes and their association with immune phenotypes in CCMs. An external dataset was then employed for validation. Finally, employing the Cellchat algorithm on a single-cell RNA-seq dataset, we explored potential mechanisms underlying the participation of these hub genes in cell-cell communication in CCMs. RESULTS: Our study revealed two distinct CCM subtypes with differential pattern of CRG expression and immune infiltration. Three hub genes (BTBD10, PFDN4, and CEMIP) were identified and validated, which may significantly associate with CCM pathogenesis. These genes were found to be significantly upregulated in CCM endothelial cells (ECs) and were validated through immunofluorescence and western blot analysis. Single-cell RNA-seq analysis revealed the cellular co-expression patterns of these hub genes, particularly highlighting the high expression of BTBD10 and PFDN4 in ECs. Additionally, a significant co-localization was also observed between BTBD10 and the pivotal cuproptosis gene FDX1 in Mki67+ tip cells, indicating the crucial role of cuproptosis for angiogenesis in CCMs. The study also explored the cell-cell communication between subcluster of ECs expressing these hub genes and immune cells, particularly M2 macrophages, suggesting a role for these interactions in CCM pathogenesis. CONCLUSION: This study identifies molecular signatures linking cuproptosis to CCMs pathogenesis. Three hub genes-PFDN4, CEMIP, and BTBD10-may influence disease progression by modulating immunity. Further research is needed to understand their precise disease mechanisms and evaluate their potential as biomarkers or therapeutic targets for CCMs.

Identification of cuproptosis-related miRNAs in triple-negative breast cancer and analysis of the miRNA-mRNA regulatory network.

Introduction: The close association between cuproptosis and tumor immunity in triple-negative breast cancer (TNBC) allows its monitoring for predicting the prognosis of patients with TNBC. Nevertheless, the biological function and prognostic value of cuproptosis-related miRNAs and their target genes have not been reported. Purpose: To construct the miRNA and mRNA-based risk models associated with cuproptosis for patients with TNBC. Methods: Comparison of expression levels for genes associated with cuproptosis was executed between patients in the normal individuals and the TCGA-TNBC cohort. Conducting differential analysis resulted in the identification of differentially expressed miRNA (DE-miRNAs) and differentially expressed genes (DEGs) between the TNBC and Control samples. Screening for prognostic miRNAs and biomarkers involved employing univariate Cox analysis and least absolute shrinkage and selection operator regression analyses. These methods were utilized to construct risk models aimed at predicting the survival of patients with TNBC. Based on the median value of risk scores, patients were then stratified into low- and high-risk groups. Functional enrichment analysis was employed to explore the potential function and pathways of prognostic genes. Additionally, independent prognostic analysis was performed through univariate and multivariate Cox regression. Immune infiltration analysis was performed to examine disparities in the infiltration of immune cells between the two risk groups. Finally, the prognostic gene expression was mined in key cell types of TNBC. Results: We obtained 5213 DEGs and 204 DE-miRNAs related to cuproptosis between TNBC and Control samples. Five prognostic miRNAs (miR-203a-3p, miR-1277-3p, miR-135b-5p, miR-200c-3p, and miR-592) and three biomarkers (DENND5B, IGF1R, and MEF2C) were closely associated with TNBC. Significant differences in the functions of prognostic genes between the two risk groups were observed, encompassing adipogenesis, inflammatory response, and hormone metabolic process. The prognostic gene regulatory network revealed that miR200C-3p regulated ZFPM2 and CFL2, and miR-1277-3p regulated BMP2 and RORA. A nomogram was created based on riskScore, cancer status, and pathologic stage to predict 1/3/5-year survival of patients with TNBC. Immune infiltration analysis indicated that the immune microenvironment may be associated with the progression of TNBC. Interestingly, prognostic genes exhibited higher expression levels in T cells, fibroblasts, endothelial cells, and monocytes compared to other cells. Conclusions: Five prognostic miRNA (miR-203a-3p, miR-1277-3p, miR-135b-5p, miR-200c-3p, and miR-592) and three biomarkers (DENND5B, IGF1R, and MEF2C) were significantly associated with TNBC, it provides new therapeutic targets for the treatment and prognosis of TNBC.

Multi-omics analysis of the prognostic and biological role of cuproptosis-related gene in gastric cancer.

Background: A considerable number of gastric cancer (GC) patients cannot receive benefits from current treatments. We aimed to identify possible biomarkers of cuproptosis-related genes (CRGs) in GC patients, which may help guide precision medicine-based decision-making. Methods: RNA sequencing data, copy number variations (CNVs) data, and single nucleotide variant (SNV) data were obtained from The Cancer Genome Atlas (TCGA) database and Gene Set Cancer Analysis (GSCA) database. Chi-squared test was adopted to screen differentially expressed CRGs (DE-CRGs) between samples from 14 kinds of carcinoma and adjacent tissue samples. Then, GC samples were divided into high- and low-expressed groups based on DE-CRGs for further survival analyses and the selection of biomarkers. Methylation sites related with biomarkers were acquired. The correlation between immune cells and biomarkers was verified. Finally, miRNA-mRNA, TFs-mRNA, and co-expression networks were established to detect factors with regulating effects on biomarkers. Results: Three CRGs including LIAS, GLS, and CDKN2A were identified as biomarkers in GC patients. Three methylation sites with a significant survival effect including cg13601799, 07562918, and 07253264 were acquired. Then, we found that B cells native was significantly correlated with CDKN2A, four immune cells such as T cells regulatory are significantly correlated with GLS, and two immune cells such as T cells CD4 memory activated were significantly correlated with LIAS. Moreover, 10 miRNAs in the miRNA-mRNA network and three transcription factors (TFs) in the TFs-mRNA network had a significant correlation with overall survival (OS). Finally, 20 enrichment functions were obtained on the basis of the co-expression network. Conclusions: Three biomarkers with a prognosis prediction value of GC were found, and multi-factor regulatory networks were constructed to screen out 13 factors with regulating influences of biomarkers.

Prognostic value and immunological function of cuproptosis-related genes in lung adenocarcinoma.

Background: Lung cancer is one of the malignant tumors with the highest morbidity and fatality rates worldwide. The overall survival (OS) of lung adenocarcinoma (LUAD) is poor. Cuproptosis is a copper-triggered modality of mitochondrial cell death, however, its contribution to the emergence of lung cancer is unknown. The clinical implication and immunological function of cuproptosis-related genes (CRGs) in LUAD has yet to be established. Methods: TCGA, HPA, GEPIA, Kaplan-Meier, TIMER and CancerSEA database were used to explore the prognostic value and biological function of CRGs in LUAD. Results: CRGs are primarily involved in copper ion transport, the citrate cycle (TCA cycle) and central carbon metabolism in LUAD. The mRNA expression of COA6, UBE2D1, DLAT, SLC25A3, and DBH was significantly increased. The expression of COA6, DLAT, SLC25A3, DBH, and LOXL2 were all strongly associated with the clinicopathological stages in LUAD. Moreover, high expression of COA6, UBE2D1, DLAT, SLC25A3 and LOXL2 was related to poor OS. The expression of SLC25A3 and LOXL2 showed different association with immune cell infiltration. The single cell sequencing demonstrated that CRGs play important roles in the regulation of DNA damage response, inflammation and metastasis in LUAD. Conclusions: In summary, this study comprehensively uncovered that CRGs could be identified as potential prognostic and immunological biomarkers in LUAD. Our current research could provide a solid theoretical basis for LUAD survival research and clinical decision-making.

Cuproptosis gene-related, neural network-based prognosis prediction and drug-target prediction for KIRC.

BACKGROUND: Kidney renal clear cell carcinoma (KIRC), as a common case in renal cell carcinoma (RCC), has the risk of postoperative recurrence, thus its prognosis is poor and its prognostic markers are usually based on imaging methods, which have the problem of low specificity. In addition, cuproptosis, as a novel mode of cell death, has been used as a biomarker to predict disease in many cancers in recent years, which also provides an important basis for prognostic prediction in KIRC. For postoperative patients with KIRC, an important means of preventing disease recurrence is pharmacological treatment, and thus matching the appropriate drug to the specific patient's target is also particularly important. With the development of neural networks, their predictive performance in the field of medical big data has surpassed that of traditional methods, and this also applies to the field of prognosis prediction and drug-target prediction. OBJECTIVE: The purpose of this study is to screen for cuproptosis genes related to the prognosis of KIRC and to establish a deep neural network (DNN) model for patient risk prediction, while also developing a personalized nomogram model for predicting patient survival. In addition, sensitivity drugs for KIRC were screened, and a graph neural network (GNN) model was established to predict the targets of the drugs, in order to discover potential drug action sites and provide new treatment ideas for KIRC. METHODS: We used the Cancer Genome Atlas (TCGA) database, International Cancer Genome Consortium (ICGC) database, and DrugBank database for our study. Differentially expressed genes (DEGs) were screened using TCGA data, and then a DNN-based risk prediction model was built and validated using ICGC data. Subsequently, the differences between high- and low-risk groups were analyzed and KIRC-sensitive drugs were screened, and finally a GNN model was trained using DrugBank data to predict the relevant targets of these drugs. RESULTS: A prognostic model was built by screening 10 significantly different cuproptosis-related genes, the model had an AUC of 0.739 on the training set (TCGA data) and an AUC of 0.707 on the validation set (ICGC data), which demonstrated a good predictive performance. Based on the prognostic model in this paper, patients were also classified into high- and low-risk groups, and functional analyses were performed. In addition, 251 drugs were screened for sensitivity, and four drugs were ultimately found to have high sensitivity, with 5-Fluorouracil having the best inhibitory effect, and subsequently their corresponding targets were also predicted by GraphSAGE, with the most prominent targets including Cytochrome P450 2D6, UDP-glucuronosyltransferase 1A, and Proto-oncogene tyrosine-protein kinase receptor Ret. Notably, the average accuracy of GraphSAGE was 0.817 ± 0.013, which was higher than that of GAT and GTN. CONCLUSION: Our KIRC risk prediction model, constructed using 10 cuproptosis-related genes, had good independent prognostic ability. In addition, we screened four highly sensitive drugs and predicted relevant targets for these four drugs that might treat KIRC. Finally, literature research revealed that four drug-target interactions have been demonstrated in previous studies and the remaining targets are potential sites of drug action for future research.

Progress in the research of cuproptosis and possible targets for cancer therapy.

Developing novel cancer therapies that exploit programmed cell death pathways holds promise for advancing cancer treatment. According to a recently published study in Science, copper death (cuproptosis) occurs when intracellular copper is overloaded, triggering aggregation of lipidated mitochondrial proteins and Fe-S cluster proteins. This intriguing phenomenon is triggered by the instability of copper ions. Understanding the molecular mechanisms behind cuproptosis and its associated genes, as identified by Tsvetkov, including ferredoxin 1, lipoic acid synthase, lipoyltransferase 1, dihydrolipid amide dehydrogenase, dihydrolipoamide transacetylase, pyruvate dehydrogenase α1, pyruvate dehydrogenase ß, metallothionein, glutaminase, and cyclin-dependent kinase inhibitor 2A, may open new avenues for cancer therapy. Here, we provide a new understanding of the role of copper death and related genes in cancer.

Cuproptosis and cuproptosis-related genes: Emerging potential therapeutic targets in breast cancer.

Breast cancer is one of the most common malignant tumors in women worldwide, and thus, it is important to enhance its treatment efficacy [1]. Copper has emerged as a critical trace element that affects various intracellular signaling pathways, gene expression, and biological metabolic processes [2], thereby playing a crucial role in the pathogenesis of breast cancer. Recent studies have identified cuproptosis, a newly discovered type of cell death, as an emerging therapeutic target for breast cancer treatment, thereby offering new hope for breast cancer patients. Tsvetkov's research has elucidated the mechanism of cuproptosis and uncovered the critical genes involved in its regulation [3]. Manipulating the expression of these genes could potentially serve as a promising therapeutic strategy for breast cancer treatment. Additionally, using copper ionophores and copper complexes combined with nanomaterials to induce cuproptosis may provide a potential approach to eliminating drug-resistant breast cancer cells, thus improving the therapeutic efficacy of chemotherapy, radiotherapy, and immunotherapy and eventually eradicating breast tumors. This review aims to highlight the practical significance of cuproptosis-related genes and the induction of cuproptosis in the clinical diagnosis and treatment of breast cancer. We examine the potential of cuproptosis as a novel therapeutic target for breast cancer, and we explore the present challenges and limitations of this approach. Our objective is to provide innovative ideas and references for the development of breast cancer treatment strategies based on cuproptosis.

Cuproptosis-a potential target for the treatment of osteoporosis.

Osteoporosis is an age-related disease of bone metabolism marked by reduced bone mineral density and impaired bone strength. The disease causes the bones to weaken and break more easily. Osteoclasts participate in bone resorption more than osteoblasts participate in bone formation, disrupting bone homeostasis and leading to osteoporosis. Currently, drug therapy for osteoporosis includes calcium supplements, vitamin D, parathyroid hormone, estrogen, calcitonin, bisphosphates, and other medications. These medications are effective in treating osteoporosis but have side effects. Copper is a necessary trace element in the human body, and studies have shown that it links to the development of osteoporosis. Cuproptosis is a recently proposed new type of cell death. Copper-induced cell death regulates by lipoylated components mediated via mitochondrial ferredoxin 1; that is, copper binds directly to the lipoylated components of the tricarboxylic acid cycle, resulting in lipoylated protein accumulation and subsequent loss of iron-sulfur cluster proteins, leading to proteotoxic stress and eventually cell death. Therapeutic options for tumor disorders include targeting the intracellular toxicity of copper and cuproptosis. The hypoxic environment in bone and the metabolic pathway of glycolysis to provide energy in cells can inhibit cuproptosis, which may promote the survival and proliferation of various cells, including osteoblasts, osteoclasts, effector T cells, and macrophages, thereby mediating the osteoporosis process. As a result, our group tried to explain the relationship between the role of cuproptosis and its essential regulatory genes, as well as the pathological mechanism of osteoporosis and its effects on various cells. This study intends to investigate a new treatment approach for the clinical treatment of osteoporosis that is beneficial to the treatment of osteoporosis.

A nomogram based on cuproptosis-related genes predicts 7-year relapse-free survival in patients with estrogen receptor-positive early breast cancer.

Introduction: Excess copper induces cell death by binding to lipoylated components of the tricarboxylic acid cycle. Although a few studies have examined the relationship between cuproptosis-related genes (CRGs) and breast cancer prognosis, reports on estrogen receptor-positive (ER+) breast cancer are lacking. Herein, we aimed to analyze the relationship between CRGs and outcomes in patients with ER+ early breast cancer (EBC). Methods: We conducted a case-control study among patients with ER+ EBC presenting poor and favorable invasive disease-free survival (iDFS) at West China Hospital. Logistic regression analysis was performed to establish the association between CRG expression and iDFS. A cohort study was performed using pooled data from three publicly available microarray datasets in the Gene Expression Omnibus database. Subsequently, we constructed a CRG score model and a nomogram to predict relapse-free survival (RFS). Finally, the prediction performance of the two models was verified using training and validation sets. Results: In this case-control study, high expression of LIAS, LIPT1, and ATP7B and low CDKN2A expression were associated with favorable iDFS. In the cohort study, high expression of FDX1, LIAS, LIPT1, DLD, PDHB, and ATP7B and low CDKN2A expression were associated with favorable RFS. Using LASSO-Cox analysis, a CRG score was developed using the seven identified CRGs. Patients in the low CRG score group had a reduced risk of relapse in both training and validation sets. The nomogram included the CRG score, lymph node status, and age. The area under the receiver operating characteristic (ROC) curve (AUC) of the nomogram was significantly higher than the AUC of the CRG score at 7 years. Conclusions: The CRG score, combined with other clinical features, could afford a practical long-term outcome predictor in patients with ER+ EBC.